Overview

Dataset statistics

Number of variables25
Number of observations4703
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory955.3 KiB
Average record size in memory208.0 B

Variable types

Text7
Numeric16
Categorical2

Alerts

content_rating is highly imbalanced (50.7%)Imbalance
budget is highly skewed (γ1 = 49.02395721)Skewed
director_fb_likes has 825 (17.5%) zerosZeros
actor_3_fb_likes has 66 (1.4%) zerosZeros
facenumber_in_poster has 2019 (42.9%) zerosZeros
movie_fb_likes has 2086 (44.4%) zerosZeros

Reproduction

Analysis started2024-04-11 08:27:22.236278
Analysis finished2024-04-11 08:28:10.481096
Duration48.24 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct2244
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:10.819096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length24
Mean length13.085477
Min length3

Characters and Unicode

Total characters61541
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1378 ?
Unique (%)29.3%

Sample

1st rowJames Cameron
2nd rowGore Verbinski
3rd rowSam Mendes
4th rowChristopher Nolan
5th rowAndrew Stanton
ValueCountFrequency (%)
john 173
 
1.8%
david 143
 
1.5%
michael 123
 
1.3%
james 85
 
0.9%
peter 83
 
0.8%
robert 81
 
0.8%
richard 79
 
0.8%
paul 73
 
0.7%
scott 65
 
0.7%
steven 57
 
0.6%
Other values (2797) 8812
90.2%
2024-04-11T10:28:11.728132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5822
 
9.5%
5071
 
8.2%
a 4984
 
8.1%
n 4437
 
7.2%
r 4250
 
6.9%
o 3622
 
5.9%
i 3505
 
5.7%
l 2831
 
4.6%
t 2213
 
3.6%
s 1985
 
3.2%
Other values (66) 22821
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61541
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5822
 
9.5%
5071
 
8.2%
a 4984
 
8.1%
n 4437
 
7.2%
r 4250
 
6.9%
o 3622
 
5.9%
i 3505
 
5.7%
l 2831
 
4.6%
t 2213
 
3.6%
s 1985
 
3.2%
Other values (66) 22821
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61541
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5822
 
9.5%
5071
 
8.2%
a 4984
 
8.1%
n 4437
 
7.2%
r 4250
 
6.9%
o 3622
 
5.9%
i 3505
 
5.7%
l 2831
 
4.6%
t 2213
 
3.6%
s 1985
 
3.2%
Other values (66) 22821
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61541
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5822
 
9.5%
5071
 
8.2%
a 4984
 
8.1%
n 4437
 
7.2%
r 4250
 
6.9%
o 3622
 
5.9%
i 3505
 
5.7%
l 2831
 
4.6%
t 2213
 
3.6%
s 1985
 
3.2%
Other values (66) 22821
37.1%

num_critic_for_reviews
Real number (ℝ)

Distinct527
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.771
Minimum1
Maximum813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:11.992095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q157
median117
Q3200
95-th percentile391
Maximum813
Range812
Interquartile range (IQR)143

Descriptive statistics

Standard deviation120.99175
Coefficient of variation (CV)0.83001251
Kurtosis2.8708933
Mean145.771
Median Absolute Deviation (MAD)68
Skewness1.5050244
Sum685561
Variance14639.004
MonotonicityNot monotonic
2024-04-11T10:28:12.248096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 32
 
0.7%
43 30
 
0.6%
63 29
 
0.6%
25 29
 
0.6%
97 28
 
0.6%
50 28
 
0.6%
29 28
 
0.6%
16 28
 
0.6%
64 28
 
0.6%
112 28
 
0.6%
Other values (517) 4415
93.9%
ValueCountFrequency (%)
1 21
0.4%
2 17
0.4%
3 9
 
0.2%
4 14
0.3%
5 26
0.6%
6 15
0.3%
7 18
0.4%
8 24
0.5%
9 24
0.5%
10 26
0.6%
ValueCountFrequency (%)
813 1
< 0.1%
775 1
< 0.1%
765 1
< 0.1%
750 2
< 0.1%
739 1
< 0.1%
738 1
< 0.1%
733 1
< 0.1%
723 1
< 0.1%
712 1
< 0.1%
703 1
< 0.1%

duration
Real number (ℝ)

Distinct164
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.63066
Minimum14
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:12.532094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile84
Q194
median104
Q3118
95-th percentile146
Maximum330
Range316
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.562204
Coefficient of variation (CV)0.20769646
Kurtosis11.779179
Mean108.63066
Median Absolute Deviation (MAD)11
Skewness2.2280838
Sum510890
Variance509.05305
MonotonicityNot monotonic
2024-04-11T10:28:12.785100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 143
 
3.0%
100 134
 
2.8%
98 130
 
2.8%
101 130
 
2.8%
97 125
 
2.7%
93 120
 
2.6%
99 120
 
2.6%
94 120
 
2.6%
95 119
 
2.5%
106 108
 
2.3%
Other values (154) 3454
73.4%
ValueCountFrequency (%)
14 1
< 0.1%
20 1
< 0.1%
25 1
< 0.1%
34 1
< 0.1%
37 1
< 0.1%
41 1
< 0.1%
45 2
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
53 1
< 0.1%
ValueCountFrequency (%)
330 1
< 0.1%
325 1
< 0.1%
300 1
< 0.1%
293 1
< 0.1%
289 1
< 0.1%
280 1
< 0.1%
271 1
< 0.1%
270 1
< 0.1%
251 2
< 0.1%
240 2
< 0.1%

director_fb_likes
Real number (ℝ)

ZEROS 

Distinct429
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.17223
Minimum0
Maximum23000
Zeros825
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:13.134093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median52
Q3209
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)201

Descriptive statistics

Standard deviation2861.8195
Coefficient of variation (CV)4.0297542
Kurtosis26.029513
Mean710.17223
Median Absolute Deviation (MAD)52
Skewness5.1181934
Sum3339940
Variance8190010.9
MonotonicityNot monotonic
2024-04-11T10:28:13.352065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 825
 
17.5%
3 65
 
1.4%
6 61
 
1.3%
7 58
 
1.2%
11 56
 
1.2%
2 56
 
1.2%
4 54
 
1.1%
10 51
 
1.1%
12 48
 
1.0%
5 48
 
1.0%
Other values (419) 3381
71.9%
ValueCountFrequency (%)
0 825
17.5%
2 56
 
1.2%
3 65
 
1.4%
4 54
 
1.1%
5 48
 
1.0%
6 61
 
1.3%
7 58
 
1.2%
8 47
 
1.0%
9 46
 
1.0%
10 51
 
1.1%
ValueCountFrequency (%)
23000 1
 
< 0.1%
22000 8
 
0.2%
21000 10
 
0.2%
18000 4
 
0.1%
17000 20
0.4%
16000 28
0.6%
15000 2
 
< 0.1%
14000 30
0.6%
13000 26
0.6%
12000 17
0.4%

actor_3_fb_likes
Real number (ℝ)

ZEROS 

Distinct905
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean662.18222
Minimum0
Maximum23000
Zeros66
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:13.542150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q1141
median383
Q3642
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)501

Descriptive statistics

Standard deviation1686.4062
Coefficient of variation (CV)2.5467404
Kurtosis57.760531
Mean662.18222
Median Absolute Deviation (MAD)251
Skewness7.1083848
Sum3114243
Variance2843966
MonotonicityNot monotonic
2024-04-11T10:28:13.730151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 123
 
2.6%
0 66
 
1.4%
11000 29
 
0.6%
2000 27
 
0.6%
3000 26
 
0.6%
3 22
 
0.5%
826 21
 
0.4%
7 21
 
0.4%
249 19
 
0.4%
322 18
 
0.4%
Other values (895) 4331
92.1%
ValueCountFrequency (%)
0 66
1.4%
2 16
 
0.3%
3 22
 
0.5%
4 18
 
0.4%
5 12
 
0.3%
6 17
 
0.4%
7 21
 
0.4%
8 15
 
0.3%
9 14
 
0.3%
10 12
 
0.3%
ValueCountFrequency (%)
23000 2
 
< 0.1%
20000 1
 
< 0.1%
19000 4
 
0.1%
17000 1
 
< 0.1%
16000 3
 
0.1%
15000 1
 
< 0.1%
14000 6
 
0.1%
13000 5
 
0.1%
12000 7
 
0.1%
11000 29
0.6%
Distinct2824
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:14.160110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length28
Median length25
Mean length13.069105
Min length3

Characters and Unicode

Total characters61464
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1928 ?
Unique (%)41.0%

Sample

1st rowJoel David Moore
2nd rowOrlando Bloom
3rd rowRory Kinnear
4th rowChristian Bale
5th rowSamantha Morton
ValueCountFrequency (%)
michael 94
 
1.0%
david 53
 
0.5%
john 53
 
0.5%
james 50
 
0.5%
tom 48
 
0.5%
scott 48
 
0.5%
jason 42
 
0.4%
robert 41
 
0.4%
kevin 39
 
0.4%
adam 36
 
0.4%
Other values (3619) 9221
94.8%
2024-04-11T10:28:14.868078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5801
 
9.4%
a 5523
 
9.0%
5022
 
8.2%
n 4439
 
7.2%
r 4122
 
6.7%
i 3777
 
6.1%
o 3405
 
5.5%
l 3204
 
5.2%
t 2192
 
3.6%
s 2018
 
3.3%
Other values (68) 21961
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5801
 
9.4%
a 5523
 
9.0%
5022
 
8.2%
n 4439
 
7.2%
r 4122
 
6.7%
i 3777
 
6.1%
o 3405
 
5.5%
l 3204
 
5.2%
t 2192
 
3.6%
s 2018
 
3.3%
Other values (68) 21961
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5801
 
9.4%
a 5523
 
9.0%
5022
 
8.2%
n 4439
 
7.2%
r 4122
 
6.7%
i 3777
 
6.1%
o 3405
 
5.5%
l 3204
 
5.2%
t 2192
 
3.6%
s 2018
 
3.3%
Other values (68) 21961
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5801
 
9.4%
a 5523
 
9.0%
5022
 
8.2%
n 4439
 
7.2%
r 4122
 
6.7%
i 3777
 
6.1%
o 3405
 
5.5%
l 3204
 
5.2%
t 2192
 
3.6%
s 2018
 
3.3%
Other values (68) 21961
35.7%

actor_1_fb_likes
Real number (ℝ)

Distinct843
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6817.3957
Minimum0
Maximum640000
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:15.085384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile116.1
Q1637
median1000
Q311000
95-th percentile24000
Maximum640000
Range640000
Interquartile range (IQR)10363

Descriptive statistics

Standard deviation14982.445
Coefficient of variation (CV)2.1976786
Kurtosis720.98565
Mean6817.3957
Median Absolute Deviation (MAD)790
Skewness19.549467
Sum32062212
Variance2.2447365 × 108
MonotonicityNot monotonic
2024-04-11T10:28:15.284383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 417
 
8.9%
11000 207
 
4.4%
2000 187
 
4.0%
3000 148
 
3.1%
12000 133
 
2.8%
13000 126
 
2.7%
14000 121
 
2.6%
10000 109
 
2.3%
18000 108
 
2.3%
22000 79
 
1.7%
Other values (833) 3068
65.2%
ValueCountFrequency (%)
0 14
0.3%
2 6
0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 4
 
0.1%
6 3
 
0.1%
7 2
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
12 3
 
0.1%
ValueCountFrequency (%)
640000 1
 
< 0.1%
260000 2
 
< 0.1%
164000 2
 
< 0.1%
137000 2
 
< 0.1%
87000 8
 
0.2%
77000 1
 
< 0.1%
49000 27
0.6%
46000 1
 
< 0.1%
45000 5
 
0.1%
44000 2
 
< 0.1%

gross
Real number (ℝ)

Distinct4146
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45085643
Minimum162
Maximum7.6050585 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:15.472080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile100669.6
Q16494675
median24848292
Q354548936
95-th percentile1.7099911 × 108
Maximum7.6050585 × 108
Range7.6050568 × 108
Interquartile range (IQR)48054262

Descriptive statistics

Standard deviation64148103
Coefficient of variation (CV)1.4228055
Kurtosis16.705866
Mean45085643
Median Absolute Deviation (MAD)20807704
Skewness3.3289438
Sum2.1203778 × 1011
Variance4.1149791 × 1015
MonotonicityNot monotonic
2024-04-11T10:28:16.017605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24848292 458
 
9.7%
5000000 4
 
0.1%
3000000 3
 
0.1%
218051260 3
 
0.1%
177343675 3
 
0.1%
8000000 3
 
0.1%
13401683 2
 
< 0.1%
800000 2
 
< 0.1%
22494487 2
 
< 0.1%
21028755 2
 
< 0.1%
Other values (4136) 4221
89.8%
ValueCountFrequency (%)
162 1
< 0.1%
423 1
< 0.1%
607 1
< 0.1%
703 1
< 0.1%
721 1
< 0.1%
728 1
< 0.1%
828 1
< 0.1%
1029 1
< 0.1%
1100 1
< 0.1%
1111 1
< 0.1%
ValueCountFrequency (%)
760505847 1
< 0.1%
658672302 1
< 0.1%
652177271 1
< 0.1%
623279547 1
< 0.1%
533316061 1
< 0.1%
474544677 1
< 0.1%
460935665 1
< 0.1%
458991599 1
< 0.1%
448130642 1
< 0.1%
436471036 1
< 0.1%

genres
Text

Distinct878
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:16.254605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length64
Median length53
Mean length20.563045
Min length5

Characters and Unicode

Total characters96708
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique482 ?
Unique (%)10.2%

Sample

1st rowAction|Adventure|Fantasy|Sci-Fi
2nd rowAction|Adventure|Fantasy
3rd rowAction|Adventure|Thriller
4th rowAction|Thriller
5th rowAction|Adventure|Sci-Fi
ValueCountFrequency (%)
drama 209
 
4.4%
comedy 186
 
4.0%
comedy|drama|romance 182
 
3.9%
comedy|drama 180
 
3.8%
comedy|romance 150
 
3.2%
drama|romance 147
 
3.1%
crime|drama|thriller 94
 
2.0%
horror 64
 
1.4%
action|crime|thriller 63
 
1.3%
action|crime|drama|thriller 63
 
1.3%
Other values (868) 3365
71.6%
2024-04-11T10:28:16.702657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 9885
 
10.2%
| 8982
 
9.3%
a 8493
 
8.8%
e 7510
 
7.8%
m 6934
 
7.2%
i 6237
 
6.4%
o 5991
 
6.2%
y 4360
 
4.5%
n 4267
 
4.4%
t 3808
 
3.9%
Other values (23) 30241
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 9885
 
10.2%
| 8982
 
9.3%
a 8493
 
8.8%
e 7510
 
7.8%
m 6934
 
7.2%
i 6237
 
6.4%
o 5991
 
6.2%
y 4360
 
4.5%
n 4267
 
4.4%
t 3808
 
3.9%
Other values (23) 30241
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 9885
 
10.2%
| 8982
 
9.3%
a 8493
 
8.8%
e 7510
 
7.8%
m 6934
 
7.2%
i 6237
 
6.4%
o 5991
 
6.2%
y 4360
 
4.5%
n 4267
 
4.4%
t 3808
 
3.9%
Other values (23) 30241
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 9885
 
10.2%
| 8982
 
9.3%
a 8493
 
8.8%
e 7510
 
7.8%
m 6934
 
7.2%
i 6237
 
6.4%
o 5991
 
6.2%
y 4360
 
4.5%
n 4267
 
4.4%
t 3808
 
3.9%
Other values (23) 30241
31.3%
Distinct1928
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:17.090685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length27
Median length24
Mean length13.182011
Min length4

Characters and Unicode

Total characters61995
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1237 ?
Unique (%)26.3%

Sample

1st rowCCH Pounder
2nd rowJohnny Depp
3rd rowChristoph Waltz
4th rowTom Hardy
5th rowDaryl Sabara
ValueCountFrequency (%)
robert 106
 
1.1%
tom 90
 
0.9%
michael 83
 
0.9%
de 56
 
0.6%
jason 53
 
0.5%
steve 50
 
0.5%
james 50
 
0.5%
bruce 49
 
0.5%
jr 48
 
0.5%
niro 48
 
0.5%
Other values (2681) 9114
93.5%
2024-04-11T10:28:17.670471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5828
 
9.4%
a 5320
 
8.6%
5044
 
8.1%
n 4504
 
7.3%
r 4010
 
6.5%
i 3953
 
6.4%
o 3645
 
5.9%
l 3086
 
5.0%
t 2427
 
3.9%
s 2194
 
3.5%
Other values (65) 21984
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5828
 
9.4%
a 5320
 
8.6%
5044
 
8.1%
n 4504
 
7.3%
r 4010
 
6.5%
i 3953
 
6.4%
o 3645
 
5.9%
l 3086
 
5.0%
t 2427
 
3.9%
s 2194
 
3.5%
Other values (65) 21984
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5828
 
9.4%
a 5320
 
8.6%
5044
 
8.1%
n 4504
 
7.3%
r 4010
 
6.5%
i 3953
 
6.4%
o 3645
 
5.9%
l 3086
 
5.0%
t 2427
 
3.9%
s 2194
 
3.5%
Other values (65) 21984
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5828
 
9.4%
a 5320
 
8.6%
5044
 
8.1%
n 4504
 
7.3%
r 4010
 
6.5%
i 3953
 
6.4%
o 3645
 
5.9%
l 3086
 
5.0%
t 2427
 
3.9%
s 2194
 
3.5%
Other values (65) 21984
35.5%
Distinct4624
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:18.050473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length87
Median length59
Mean length16.306613
Min length2

Characters and Unicode

Total characters76690
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4547 ?
Unique (%)96.7%

Sample

1st rowAvatar 
2nd rowPirates of the Caribbean: At World's End 
3rd rowSpectre 
4th rowThe Dark Knight Rises 
5th rowJohn Carter 
ValueCountFrequency (%)
the 1499
 
11.5%
of 449
 
3.4%
a 173
 
1.3%
and 136
 
1.0%
in 115
 
0.9%
2 103
 
0.8%
to 98
 
0.8%
75
 
0.6%
man 64
 
0.5%
love 53
 
0.4%
Other values (4679) 10265
78.8%
2024-04-11T10:28:18.670143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8327
 
10.9%
e 7367
 
9.6%
  4703
 
6.1%
a 4512
 
5.9%
o 4351
 
5.7%
r 3863
 
5.0%
n 3846
 
5.0%
i 3689
 
4.8%
t 3562
 
4.6%
s 2816
 
3.7%
Other values (82) 29654
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8327
 
10.9%
e 7367
 
9.6%
  4703
 
6.1%
a 4512
 
5.9%
o 4351
 
5.7%
r 3863
 
5.0%
n 3846
 
5.0%
i 3689
 
4.8%
t 3562
 
4.6%
s 2816
 
3.7%
Other values (82) 29654
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8327
 
10.9%
e 7367
 
9.6%
  4703
 
6.1%
a 4512
 
5.9%
o 4351
 
5.7%
r 3863
 
5.0%
n 3846
 
5.0%
i 3689
 
4.8%
t 3562
 
4.6%
s 2816
 
3.7%
Other values (82) 29654
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8327
 
10.9%
e 7367
 
9.6%
  4703
 
6.1%
a 4512
 
5.9%
o 4351
 
5.7%
r 3863
 
5.0%
n 3846
 
5.0%
i 3689
 
4.8%
t 3562
 
4.6%
s 2816
 
3.7%
Other values (82) 29654
38.7%

num_voted_users
Real number (ℝ)

Distinct4593
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87783.318
Minimum5
Maximum1689764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:18.871447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1099.4
Q110774
median37952
Q3101938
95-th percentile343205.1
Maximum1689764
Range1689759
Interquartile range (IQR)91164

Descriptive statistics

Standard deviation140733.28
Coefficient of variation (CV)1.6031894
Kurtosis23.651174
Mean87783.318
Median Absolute Deviation (MAD)32809
Skewness3.9557772
Sum4.1284494 × 108
Variance1.9805856 × 1010
MonotonicityNot monotonic
2024-04-11T10:28:19.057485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3119 3
 
0.1%
2541 3
 
0.1%
3665 3
 
0.1%
9903 2
 
< 0.1%
6069 2
 
< 0.1%
80639 2
 
< 0.1%
25870 2
 
< 0.1%
1231 2
 
< 0.1%
3943 2
 
< 0.1%
53 2
 
< 0.1%
Other values (4583) 4680
99.5%
ValueCountFrequency (%)
5 2
< 0.1%
19 1
< 0.1%
28 1
< 0.1%
37 1
< 0.1%
40 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
50 1
< 0.1%
53 2
< 0.1%
59 1
< 0.1%
ValueCountFrequency (%)
1689764 1
< 0.1%
1676169 1
< 0.1%
1468200 1
< 0.1%
1347461 1
< 0.1%
1324680 1
< 0.1%
1251222 1
< 0.1%
1238746 1
< 0.1%
1217752 1
< 0.1%
1215718 1
< 0.1%
1155770 1
< 0.1%

cast_total_fb_likes
Real number (ℝ)

Distinct3841
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10073.469
Minimum0
Maximum656730
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:19.240490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile243
Q11500.5
median3227
Q314462.5
95-th percentile37605.9
Maximum656730
Range656730
Interquartile range (IQR)12962

Descriptive statistics

Standard deviation18234.161
Coefficient of variation (CV)1.8101174
Kurtosis372.68719
Mean10073.469
Median Absolute Deviation (MAD)2418
Skewness12.956554
Sum47375524
Variance3.3248463 × 108
MonotonicityNot monotonic
2024-04-11T10:28:19.426080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
0.3%
2020 6
 
0.1%
29 5
 
0.1%
673 5
 
0.1%
1044 5
 
0.1%
2321 4
 
0.1%
1936 4
 
0.1%
1761 4
 
0.1%
2486 4
 
0.1%
1136 4
 
0.1%
Other values (3831) 4648
98.8%
ValueCountFrequency (%)
0 14
0.3%
2 4
 
0.1%
4 2
 
< 0.1%
5 3
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
15 2
 
< 0.1%
ValueCountFrequency (%)
656730 1
< 0.1%
303717 1
< 0.1%
283939 1
< 0.1%
263584 1
< 0.1%
170118 1
< 0.1%
140268 1
< 0.1%
137712 1
< 0.1%
120797 1
< 0.1%
108016 1
< 0.1%
106759 1
< 0.1%
Distinct3312
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:19.797076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length29
Median length25
Mean length13.072932
Min length3

Characters and Unicode

Total characters61482
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2494 ?
Unique (%)53.0%

Sample

1st rowWes Studi
2nd rowJack Davenport
3rd rowStephanie Sigman
4th rowJoseph Gordon-Levitt
5th rowPolly Walker
ValueCountFrequency (%)
michael 79
 
0.8%
john 71
 
0.7%
david 68
 
0.7%
james 64
 
0.7%
robert 46
 
0.5%
kevin 39
 
0.4%
paul 38
 
0.4%
tom 38
 
0.4%
peter 37
 
0.4%
steve 36
 
0.4%
Other values (4097) 9226
94.7%
2024-04-11T10:28:20.452999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5818
 
9.5%
a 5598
 
9.1%
5039
 
8.2%
n 4302
 
7.0%
r 3921
 
6.4%
i 3719
 
6.0%
o 3333
 
5.4%
l 3291
 
5.4%
t 2209
 
3.6%
s 2181
 
3.5%
Other values (71) 22071
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5818
 
9.5%
a 5598
 
9.1%
5039
 
8.2%
n 4302
 
7.0%
r 3921
 
6.4%
i 3719
 
6.0%
o 3333
 
5.4%
l 3291
 
5.4%
t 2209
 
3.6%
s 2181
 
3.5%
Other values (71) 22071
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5818
 
9.5%
a 5598
 
9.1%
5039
 
8.2%
n 4302
 
7.0%
r 3921
 
6.4%
i 3719
 
6.0%
o 3333
 
5.4%
l 3291
 
5.4%
t 2209
 
3.6%
s 2181
 
3.5%
Other values (71) 22071
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5818
 
9.5%
a 5598
 
9.1%
5039
 
8.2%
n 4302
 
7.0%
r 3921
 
6.4%
i 3719
 
6.0%
o 3333
 
5.4%
l 3291
 
5.4%
t 2209
 
3.6%
s 2181
 
3.5%
Other values (71) 22071
35.9%

facenumber_in_poster
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3567935
Minimum0
Maximum43
Zeros2019
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:20.636040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0086637
Coefficient of variation (CV)1.4804491
Kurtosis55.770377
Mean1.3567935
Median Absolute Deviation (MAD)1
Skewness4.5646736
Sum6381
Variance4.03473
MonotonicityNot monotonic
2024-04-11T10:28:20.774039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2019
42.9%
1 1179
25.1%
2 665
 
14.1%
3 359
 
7.6%
4 190
 
4.0%
5 100
 
2.1%
6 67
 
1.4%
7 45
 
1.0%
8 34
 
0.7%
9 15
 
0.3%
Other values (9) 30
 
0.6%
ValueCountFrequency (%)
0 2019
42.9%
1 1179
25.1%
2 665
 
14.1%
3 359
 
7.6%
4 190
 
4.0%
5 100
 
2.1%
6 67
 
1.4%
7 45
 
1.0%
8 34
 
0.7%
9 15
 
0.3%
ValueCountFrequency (%)
43 1
 
< 0.1%
31 1
 
< 0.1%
19 1
 
< 0.1%
15 5
 
0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
0.1%
11 5
 
0.1%
10 10
0.2%
9 15
0.3%
Distinct4620
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:21.443175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length149
Median length102
Mean length52.40825
Min length2

Characters and Unicode

Total characters246476
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4541 ?
Unique (%)96.6%

Sample

1st rowavatar|future|marine|native|paraplegic
2nd rowgoddess|marriage ceremony|marriage proposal|pirate|singapore
3rd rowbomb|espionage|sequel|spy|terrorist
4th rowdeception|imprisonment|lawlessness|police officer|terrorist plot
5th rowalien|american civil war|male nipple|mars|princess
ValueCountFrequency (%)
in 314
 
1.8%
of 212
 
1.2%
on 196
 
1.1%
the 185
 
1.1%
a 180
 
1.0%
to 174
 
1.0%
york 120
 
0.7%
female 102
 
0.6%
by 99
 
0.6%
based 98
 
0.6%
Other values (11164) 15596
90.3%
2024-04-11T10:28:22.719619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 23831
 
9.7%
a 18830
 
7.6%
| 18520
 
7.5%
i 17990
 
7.3%
r 17414
 
7.1%
t 15540
 
6.3%
n 15097
 
6.1%
o 14867
 
6.0%
s 12776
 
5.2%
12573
 
5.1%
Other values (32) 79038
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 246476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 23831
 
9.7%
a 18830
 
7.6%
| 18520
 
7.5%
i 17990
 
7.3%
r 17414
 
7.1%
t 15540
 
6.3%
n 15097
 
6.1%
o 14867
 
6.0%
s 12776
 
5.2%
12573
 
5.1%
Other values (32) 79038
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 246476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 23831
 
9.7%
a 18830
 
7.6%
| 18520
 
7.5%
i 17990
 
7.3%
r 17414
 
7.1%
t 15540
 
6.3%
n 15097
 
6.1%
o 14867
 
6.0%
s 12776
 
5.2%
12573
 
5.1%
Other values (32) 79038
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 246476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 23831
 
9.7%
a 18830
 
7.6%
| 18520
 
7.5%
i 17990
 
7.3%
r 17414
 
7.1%
t 15540
 
6.3%
n 15097
 
6.1%
o 14867
 
6.0%
s 12776
 
5.2%
12573
 
5.1%
Other values (32) 79038
32.1%

num_user_for_reviews
Real number (ℝ)

Distinct953
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.75973
Minimum1
Maximum5060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:23.344842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q175
median166
Q3340
95-th percentile921.7
Maximum5060
Range5059
Interquartile range (IQR)265

Descriptive statistics

Standard deviation383.73636
Coefficient of variation (CV)1.3475795
Kurtosis25.963836
Mean284.75973
Median Absolute Deviation (MAD)113
Skewness4.0957567
Sum1339225
Variance147253.59
MonotonicityNot monotonic
2024-04-11T10:28:23.622877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 30
 
0.6%
50 25
 
0.5%
32 24
 
0.5%
53 22
 
0.5%
21 22
 
0.5%
31 22
 
0.5%
14 22
 
0.5%
39 21
 
0.4%
10 21
 
0.4%
27 21
 
0.4%
Other values (943) 4473
95.1%
ValueCountFrequency (%)
1 18
0.4%
2 10
0.2%
3 17
0.4%
4 11
0.2%
5 14
0.3%
6 17
0.4%
7 12
0.3%
8 14
0.3%
9 17
0.4%
10 21
0.4%
ValueCountFrequency (%)
5060 1
< 0.1%
4667 1
< 0.1%
4144 1
< 0.1%
3646 1
< 0.1%
3597 1
< 0.1%
3516 1
< 0.1%
3400 1
< 0.1%
3286 1
< 0.1%
3189 1
< 0.1%
3054 1
< 0.1%

country
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
USA
3575 
Other
708 
UK
420 

Length

Max length5
Median length3
Mean length3.2117797
Min length2

Characters and Unicode

Total characters15105
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUK
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA 3575
76.0%
Other 708
 
15.1%
UK 420
 
8.9%

Length

2024-04-11T10:28:23.846838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T10:28:24.404846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
usa 3575
76.0%
other 708
 
15.1%
uk 420
 
8.9%

Most occurring characters

ValueCountFrequency (%)
U 3995
26.4%
S 3575
23.7%
A 3575
23.7%
O 708
 
4.7%
t 708
 
4.7%
h 708
 
4.7%
e 708
 
4.7%
r 708
 
4.7%
K 420
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 3995
26.4%
S 3575
23.7%
A 3575
23.7%
O 708
 
4.7%
t 708
 
4.7%
h 708
 
4.7%
e 708
 
4.7%
r 708
 
4.7%
K 420
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 3995
26.4%
S 3575
23.7%
A 3575
23.7%
O 708
 
4.7%
t 708
 
4.7%
h 708
 
4.7%
e 708
 
4.7%
r 708
 
4.7%
K 420
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 3995
26.4%
S 3575
23.7%
A 3575
23.7%
O 708
 
4.7%
t 708
 
4.7%
h 708
 
4.7%
e 708
 
4.7%
r 708
 
4.7%
K 420
 
2.8%

content_rating
Categorical

IMBALANCE 

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
R
2234 
PG-13
1419 
PG
680 
G
 
109
Not Rated
 
102
Other values (10)
 
159

Length

Max length9
Median length1
Mean length2.7016798
Min length1

Characters and Unicode

Total characters12706
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPG-13
2nd rowPG-13
3rd rowPG-13
4th rowPG-13
5th rowPG-13

Common Values

ValueCountFrequency (%)
R 2234
47.5%
PG-13 1419
30.2%
PG 680
 
14.5%
G 109
 
2.3%
Not Rated 102
 
2.2%
Unrated 57
 
1.2%
Approved 55
 
1.2%
X 13
 
0.3%
Passed 9
 
0.2%
NC-17 7
 
0.1%
Other values (5) 18
 
0.4%

Length

2024-04-11T10:28:24.689979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 2234
46.5%
pg-13 1419
29.5%
pg 680
 
14.2%
g 109
 
2.3%
not 102
 
2.1%
rated 102
 
2.1%
unrated 57
 
1.2%
approved 55
 
1.1%
x 13
 
0.3%
passed 9
 
0.2%
Other values (6) 25
 
0.5%

Most occurring characters

ValueCountFrequency (%)
R 2336
18.4%
G 2218
17.5%
P 2115
16.6%
- 1433
11.3%
1 1429
11.2%
3 1419
11.2%
t 261
 
2.1%
e 223
 
1.8%
d 223
 
1.8%
a 168
 
1.3%
Other values (17) 881
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12706
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 2336
18.4%
G 2218
17.5%
P 2115
16.6%
- 1433
11.3%
1 1429
11.2%
3 1419
11.2%
t 261
 
2.1%
e 223
 
1.8%
d 223
 
1.8%
a 168
 
1.3%
Other values (17) 881
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12706
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 2336
18.4%
G 2218
17.5%
P 2115
16.6%
- 1433
11.3%
1 1429
11.2%
3 1419
11.2%
t 261
 
2.1%
e 223
 
1.8%
d 223
 
1.8%
a 168
 
1.3%
Other values (17) 881
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12706
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 2336
18.4%
G 2218
17.5%
P 2115
16.6%
- 1433
11.3%
1 1429
11.2%
3 1419
11.2%
t 261
 
2.1%
e 223
 
1.8%
d 223
 
1.8%
a 168
 
1.3%
Other values (17) 881
 
6.9%

budget
Real number (ℝ)

SKEWED 

Distinct432
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39306827
Minimum218
Maximum1.22155 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:24.987978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile800000
Q17500000
median20000000
Q340000000
95-th percentile1.25 × 108
Maximum1.22155 × 1010
Range1.22155 × 1010
Interquartile range (IQR)32500000

Descriptive statistics

Standard deviation2.02669 × 108
Coefficient of variation (CV)5.1560762
Kurtosis2820.5211
Mean39306827
Median Absolute Deviation (MAD)15000000
Skewness49.023957
Sum1.8486001 × 1011
Variance4.1074723 × 1016
MonotonicityNot monotonic
2024-04-11T10:28:25.229981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000 442
 
9.4%
30000000 145
 
3.1%
15000000 141
 
3.0%
25000000 139
 
3.0%
10000000 137
 
2.9%
40000000 131
 
2.8%
35000000 120
 
2.6%
50000000 104
 
2.2%
5000000 102
 
2.2%
60000000 94
 
2.0%
Other values (422) 3148
66.9%
ValueCountFrequency (%)
218 1
 
< 0.1%
1100 1
 
< 0.1%
4500 1
 
< 0.1%
7000 3
0.1%
9000 1
 
< 0.1%
10000 2
< 0.1%
14000 1
 
< 0.1%
15000 2
< 0.1%
20000 3
0.1%
22000 1
 
< 0.1%
ValueCountFrequency (%)
1.22155 × 10101
< 0.1%
4200000000 1
< 0.1%
2500000000 1
< 0.1%
2400000000 1
< 0.1%
2127519898 1
< 0.1%
1100000000 1
< 0.1%
1000000000 1
< 0.1%
700000000 2
< 0.1%
600000000 1
< 0.1%
553632000 1
< 0.1%

title_year
Real number (ℝ)

Distinct91
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.1112
Minimum1916
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:25.448636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1916
5-th percentile1978
Q11999
median2005
Q32010
95-th percentile2015
Maximum2016
Range100
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.50241
Coefficient of variation (CV)0.0062446132
Kurtosis7.3909201
Mean2002.1112
Median Absolute Deviation (MAD)6
Skewness-2.2877603
Sum9415929
Variance156.31026
MonotonicityNot monotonic
2024-04-11T10:28:25.776601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2009 252
 
5.4%
2006 235
 
5.0%
2008 222
 
4.7%
2010 221
 
4.7%
2011 215
 
4.6%
2005 215
 
4.6%
2014 214
 
4.6%
2013 213
 
4.5%
2004 206
 
4.4%
2012 203
 
4.3%
Other values (81) 2507
53.3%
ValueCountFrequency (%)
1916 1
< 0.1%
1920 1
< 0.1%
1925 1
< 0.1%
1927 1
< 0.1%
1929 2
< 0.1%
1930 1
< 0.1%
1932 1
< 0.1%
1933 2
< 0.1%
1934 1
< 0.1%
1935 1
< 0.1%
ValueCountFrequency (%)
2016 82
 
1.7%
2015 183
3.9%
2014 214
4.6%
2013 213
4.5%
2012 203
4.3%
2011 215
4.6%
2010 221
4.7%
2009 252
5.4%
2008 222
4.7%
2007 197
4.2%

actor_2_fb_likes
Real number (ℝ)

Distinct906
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1718.748
Minimum0
Maximum137000
Zeros32
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:26.049108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.1
Q1298.5
median617
Q3931
95-th percentile11000
Maximum137000
Range137000
Interquartile range (IQR)632.5

Descriptive statistics

Standard deviation4136.4756
Coefficient of variation (CV)2.4066794
Kurtosis249.6205
Mean1718.748
Median Absolute Deviation (MAD)316
Skewness9.7679393
Sum8083272
Variance17110430
MonotonicityNot monotonic
2024-04-11T10:28:26.289113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 296
 
6.3%
11000 110
 
2.3%
2000 98
 
2.1%
3000 74
 
1.6%
10000 46
 
1.0%
14000 40
 
0.9%
13000 40
 
0.9%
826 37
 
0.8%
4000 33
 
0.7%
0 32
 
0.7%
Other values (896) 3897
82.9%
ValueCountFrequency (%)
0 32
0.7%
2 11
 
0.2%
3 9
 
0.2%
4 10
 
0.2%
5 8
 
0.2%
6 7
 
0.1%
7 2
 
< 0.1%
8 9
 
0.2%
9 12
 
0.3%
10 8
 
0.2%
ValueCountFrequency (%)
137000 1
 
< 0.1%
29000 1
 
< 0.1%
27000 2
 
< 0.1%
25000 3
 
0.1%
23000 6
0.1%
22000 11
0.2%
21000 3
 
0.1%
20000 6
0.1%
19000 7
0.1%
18000 9
0.2%

imdb_score
Real number (ℝ)

Distinct76
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4313842
Minimum1.6
Maximum9.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:26.523071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.4
Q15.8
median6.6
Q37.2
95-th percentile8
Maximum9.3
Range7.7
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.098782
Coefficient of variation (CV)0.17084689
Kurtosis1.0533928
Mean6.4313842
Median Absolute Deviation (MAD)0.7
Skewness-0.7682876
Sum30246.8
Variance1.2073219
MonotonicityNot monotonic
2024-04-11T10:28:26.746103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 212
 
4.5%
6.6 191
 
4.1%
6.5 181
 
3.8%
7.2 181
 
3.8%
6.4 180
 
3.8%
6.8 177
 
3.8%
7.3 175
 
3.7%
7.1 173
 
3.7%
7 173
 
3.7%
6.1 173
 
3.7%
Other values (66) 2887
61.4%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.7 1
 
< 0.1%
1.9 3
0.1%
2 2
< 0.1%
2.1 3
0.1%
2.2 3
0.1%
2.3 3
0.1%
2.4 2
< 0.1%
2.5 2
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
9.3 1
 
< 0.1%
9.2 1
 
< 0.1%
9 2
 
< 0.1%
8.9 5
 
0.1%
8.8 5
 
0.1%
8.7 8
 
0.2%
8.6 11
 
0.2%
8.5 21
0.4%
8.4 23
0.5%
8.3 34
0.7%

aspect_ratio
Real number (ℝ)

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1255305
Minimum1.18
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:26.917118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.78
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.82
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.63838629
Coefficient of variation (CV)0.3003421
Kurtosis377.18399
Mean2.1255305
Median Absolute Deviation (MAD)0
Skewness17.406589
Sum9996.37
Variance0.40753706
MonotonicityNot monotonic
2024-04-11T10:28:27.088563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2.35 2499
53.1%
1.85 1870
39.8%
1.37 97
 
2.1%
1.78 79
 
1.7%
1.66 63
 
1.3%
1.33 37
 
0.8%
2.2 14
 
0.3%
2.39 14
 
0.3%
16 8
 
0.2%
2 4
 
0.1%
Other values (10) 18
 
0.4%
ValueCountFrequency (%)
1.18 1
 
< 0.1%
1.2 1
 
< 0.1%
1.33 37
 
0.8%
1.37 97
2.1%
1.44 1
 
< 0.1%
1.5 2
 
< 0.1%
1.66 63
1.3%
1.75 3
 
0.1%
1.77 1
 
< 0.1%
1.78 79
1.7%
ValueCountFrequency (%)
16 8
 
0.2%
2.76 3
 
0.1%
2.55 2
 
< 0.1%
2.4 3
 
0.1%
2.39 14
 
0.3%
2.35 2499
53.1%
2.24 1
 
< 0.1%
2.2 14
 
0.3%
2 4
 
0.1%
1.85 1870
39.8%

movie_fb_likes
Real number (ℝ)

ZEROS 

Distinct836
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7779.7997
Minimum0
Maximum349000
Zeros2086
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-11T10:28:27.278187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median181
Q35000
95-th percentile41900
Maximum349000
Range349000
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation19611.482
Coefficient of variation (CV)2.520821
Kurtosis40.309513
Mean7779.7997
Median Absolute Deviation (MAD)181
Skewness4.9742692
Sum36588398
Variance3.8461023 × 108
MonotonicityNot monotonic
2024-04-11T10:28:27.490980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2086
44.4%
1000 103
 
2.2%
11000 80
 
1.7%
10000 79
 
1.7%
12000 59
 
1.3%
13000 58
 
1.2%
2000 54
 
1.1%
15000 51
 
1.1%
14000 46
 
1.0%
16000 46
 
1.0%
Other values (826) 2041
43.4%
ValueCountFrequency (%)
0 2086
44.4%
4 2
 
< 0.1%
5 1
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 2
 
< 0.1%
14 1
 
< 0.1%
16 1
 
< 0.1%
17 3
 
0.1%
ValueCountFrequency (%)
349000 1
< 0.1%
199000 1
< 0.1%
197000 1
< 0.1%
191000 1
< 0.1%
190000 1
< 0.1%
175000 1
< 0.1%
165000 1
< 0.1%
164000 1
< 0.1%
153000 1
< 0.1%
150000 1
< 0.1%

Interactions

2024-04-11T10:28:06.572054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:23.310077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:25.845237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:28.265509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:31.160603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:34.158700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:37.307534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:39.297301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:42.312154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:44.943981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:47.053861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:49.940174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:52.462663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:54.873611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:57.456503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:03.601575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:06.736053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:23.491806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:25.992555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:28.417716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:31.311580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:34.383938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:37.442535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:39.435306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:42.518143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:45.093479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:47.208902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:50.138186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:52.620664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:55.042811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:57.689511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:03.914577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:06.890195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:23.637807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:26.121554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:28.555714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:31.440582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:34.586565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:37.565536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:39.678257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:42.691636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:45.228517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:47.347861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:50.291184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:52.792669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:55.200816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:57.930497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:04.084574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:07.022199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:23.769808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:26.271138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:28.681035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:31.559558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:34.742023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:37.681534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:39.920164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:42.838608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:45.376838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:47.475870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:50.448982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:52.944666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:55.357811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:58.192498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:04.241574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:07.376196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:23.898804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:26.425136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:28.963997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:31.676563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:34.883534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:37.794297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:40.092155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:42.997604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:45.502064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:47.602859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:50.581980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:53.074664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:55.619811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:58.716516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:04.393576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:07.573197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:24.073841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:26.586144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:29.221999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:31.805541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:35.024573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:37.920296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:40.253015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:43.134604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:45.629062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:47.733860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:50.715980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:53.211664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:55.831812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:59.036498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:04.537576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:07.895196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:24.270805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:26.747149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:29.472003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:31.937571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:36.055588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:38.042294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:40.403013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:43.309604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:45.754064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:47.887859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:50.855924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:53.382701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:56.012810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:59.730542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:04.677577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:08.077228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:24.454809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:26.921149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:29.685000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:32.136573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:36.179536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:38.178297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:40.587051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:43.499623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:45.891064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:48.126876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:51.002881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:53.545282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:56.178452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:00.101519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:04.819574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:08.231229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:24.625840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:27.065451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:29.903000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:32.290571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:36.298534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:38.303295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:40.740014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:43.640603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:46.033070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:48.328859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:51.133927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:53.694017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:56.333449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:00.594518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:04.953442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:08.366229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:24.775807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:27.211439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:30.085045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:32.418458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:36.408538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:38.416295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:40.936016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:43.769608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:46.150812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:48.462857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:51.265551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:53.824985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:56.471449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:01.050547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:05.285794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:08.509228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:24.935806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:27.346441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:30.260043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:32.547010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:36.521539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:38.537296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:41.305017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:43.908608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:46.277810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:48.632859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:51.473549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:53.957009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:56.610450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:01.499519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:05.430781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:08.654227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:25.087806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:27.492458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:30.416041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:32.718511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:36.649534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:38.664303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:41.497132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:44.081603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:46.402857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:48.772894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:51.624549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:54.090973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:56.753448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:01.914517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:05.592793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:08.795097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:25.226807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:27.632497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:30.567866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:32.955547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:36.770535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:38.785303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:41.651136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:44.246961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:46.526868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:49.244861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:51.759549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:54.247971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:56.889449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:02.252517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:05.739784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:08.948097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:25.370807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:27.785531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:30.716352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:33.227969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:36.908535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:38.914302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:41.805136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:44.434964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:46.653860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:49.433883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:51.972550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:54.412656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:57.027449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:02.617542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:05.890795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:09.085098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:25.515805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:27.944492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:30.872298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:33.464004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:37.041536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:39.041301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:41.945134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:44.611962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:46.777855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:49.613857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:52.143548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:54.565478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:57.164450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:02.856523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:06.180824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:09.218098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:25.670235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:28.107512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:31.016298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:33.769700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:37.174537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:39.166305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:42.129136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:44.783961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:46.903859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:49.773863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:52.303665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:54.714850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:27:57.300451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:03.201518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-11T10:28:06.397820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-11T10:28:09.432138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-11T10:28:09.873104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

director_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsnum_user_for_reviewscountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes
0James Cameron723.0178.00.0855.0Joel David Moore1000.0760505847.0Action|Adventure|Fantasy|Sci-FiCCH PounderAvatar8862044834Wes Studi0.0avatar|future|marine|native|paraplegic3054.0USAPG-13237000000.02009.0936.07.91.7833000
1Gore Verbinski302.0169.0563.01000.0Orlando Bloom40000.0309404152.0Action|Adventure|FantasyJohnny DeppPirates of the Caribbean: At World's End47122048350Jack Davenport0.0goddess|marriage ceremony|marriage proposal|pirate|singapore1238.0USAPG-13300000000.02007.05000.07.12.350
2Sam Mendes602.0148.00.0161.0Rory Kinnear11000.0200074175.0Action|Adventure|ThrillerChristoph WaltzSpectre27586811700Stephanie Sigman1.0bomb|espionage|sequel|spy|terrorist994.0UKPG-13245000000.02015.0393.06.82.3585000
3Christopher Nolan813.0164.022000.023000.0Christian Bale27000.0448130642.0Action|ThrillerTom HardyThe Dark Knight Rises1144337106759Joseph Gordon-Levitt0.0deception|imprisonment|lawlessness|police officer|terrorist plot2701.0USAPG-13250000000.02012.023000.08.52.35164000
5Andrew Stanton462.0132.0475.0530.0Samantha Morton640.073058679.0Action|Adventure|Sci-FiDaryl SabaraJohn Carter2122041873Polly Walker1.0alien|american civil war|male nipple|mars|princess738.0USAPG-13263700000.02012.0632.06.62.3524000
6Sam Raimi392.0156.00.04000.0James Franco24000.0336530303.0Action|Adventure|RomanceJ.K. SimmonsSpider-Man 338305646055Kirsten Dunst0.0sandman|spider man|symbiote|venom|villain1902.0USAPG-13258000000.02007.011000.06.22.350
7Nathan Greno324.0100.015.0284.0Donna Murphy799.0200807262.0Adventure|Animation|Comedy|Family|Fantasy|Musical|RomanceBrad GarrettTangled2948102036M.C. Gainey1.017th century|based on fairy tale|disney|flower|tower387.0USAPG260000000.02010.0553.07.81.8529000
8Joss Whedon635.0141.00.019000.0Robert Downey Jr.26000.0458991599.0Action|Adventure|Sci-FiChris HemsworthAvengers: Age of Ultron46266992000Scarlett Johansson4.0artificial intelligence|based on comic book|captain america|marvel cinematic universe|superhero1117.0USAPG-13250000000.02015.021000.07.52.35118000
9David Yates375.0153.0282.010000.0Daniel Radcliffe25000.0301956980.0Adventure|Family|Fantasy|MysteryAlan RickmanHarry Potter and the Half-Blood Prince32179558753Rupert Grint3.0blood|book|love|potion|professor973.0UKPG250000000.02009.011000.07.52.3510000
10Zack Snyder673.0183.00.02000.0Lauren Cohan15000.0330249062.0Action|Adventure|Sci-FiHenry CavillBatman v Superman: Dawn of Justice37163924450Alan D. Purwin0.0based on comic book|batman|sequel to a reboot|superhero|superman3018.0USAPG-13250000000.02016.04000.06.92.35197000
director_namenum_critic_for_reviewsdurationdirector_fb_likesactor_3_fb_likesactor_2_nameactor_1_fb_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_fb_likesactor_3_namefacenumber_in_posterplot_keywordsnum_user_for_reviewscountrycontent_ratingbudgettitle_yearactor_2_fb_likesimdb_scoreaspect_ratiomovie_fb_likes
5026Olivier Assayas81.0110.0107.045.0Béatrice Dalle576.0136007.0Drama|Music|RomanceMaggie CheungClean3924776Don McKellar1.0jail|junkie|money|motel|singer39.0OtherR4500.02004.0133.06.92.35171
5027Jafar Panahi64.090.0397.00.0Nargess Mamizadeh5.0673780.0DramaFereshteh Sadre OrafaiyThe Circle45555Mojgan Faramarzi0.0abortion|bus|hospital|prison|prostitution26.0OtherNot Rated10000.02000.00.07.51.85697
5029Kiyoshi Kurosawa78.0111.062.06.0Anna Nakagawa89.094596.0Crime|Horror|Mystery|ThrillerKôji YakushoThe Cure6318115Denden0.0breasts|interrogation|investigation|murder|watching television50.0OtherR1000000.01997.013.07.41.85817
5032Ash Baron-Cohen10.098.03.0152.0Stanley B. Herman789.024848292.0Crime|DramaPeter GreeneBang4381186James Noble1.0corruption|homeless|homeless man|motorcycle|urban legend14.0USAR20000000.01995.0194.06.42.3520
5033Shane Carruth143.077.0291.08.0David Sullivan291.0424760.0Drama|Sci-Fi|ThrillerShane CarruthPrimer72639368Casey Gooden0.0changing the future|independent film|invention|nonlinear timeline|time travel371.0USAPG-137000.02004.045.07.01.8519000
5034Neill Dela Llana35.080.00.00.0Edgar Tancangco0.070071.0ThrillerIan GamazonCavite5890Quynn Ton0.0jihad|mindanao|philippines|security guard|squatter35.0OtherNot Rated7000.02005.00.06.32.3574
5035Robert Rodriguez56.081.00.06.0Peter Marquardt121.02040920.0Action|Crime|Drama|Romance|ThrillerCarlos GallardoEl Mariachi52055147Consuelo Gómez0.0assassin|death|guitar|gun|mariachi130.0USAR7000.01992.020.06.91.370
5037Edward Burns14.095.00.0133.0Caitlin FitzGerald296.04584.0Comedy|DramaKerry BishéNewlyweds1338690Daniella Pineda1.0written and directed by cast member14.0USANot Rated9000.02011.0205.06.42.35413
5038Scott Smith1.087.02.0318.0Daphne Zuniga637.024848292.0Comedy|DramaEric MabiusSigned Sealed Delivered6292283Crystal Lowe2.0fraud|postal worker|prison|theft|trial6.0OtherR20000000.02013.0470.07.72.3584
5042Jon Gunn43.090.016.016.0Brian Herzlinger86.085222.0DocumentaryJohn AugustMy Date with Drew4285163Jon Gunn0.0actress name in title|crush|date|four word title|video camera84.0USAPG1100.02004.023.06.61.85456